Matt Stiles stiles

## ne_asia.json

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                stiles
                / ne_asia.json
            
            
              Created
              October 22, 2022 01:35
            
              
                Topojson: North and South Korea
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## radio_shack_timeline.csv
id,conversation_id,text,source,in_reply_to_user_id,geo,retweet_count,reply_count,like_count,quote_count,date
1543379427505078273,1543379427505078273,"RT @spicetradeai: $1 in january is now $0.9618 due to inflation

CPI rose to 292.296 in May from 289.109 in April. To counteract inflation,…",Twitter for iPhone,,,10,0,0,0,2022-07-02
1543379333573554176,1543378085592137730,@davidjvickery Hehehehe,Twitter for iPhone,4790227152,,0,4,2,0,2022-07-02
1543378758135992320,1543378085592137730,@IcedKnife Have to keep the lights on,Twitter for iPhone,618539620,,0,0,17,0,2022-07-02
1543371121906462720,1543369905403088896,@WaspR6 Peoples champ,Twitter for iPhone,941797412380774400,,6,1,770,0,2022-07-02
1543369905403088896,1543369905403088896,Sit on my face and use my ears as handle bars.,Twitter for iPhone,,,3867,789,24854,2657,2022-07-02
1543318984933576705,1543318984933576705,"New lisitngs this week came in hot🔥

## candidates.csv

          
            candidate
            raised_in_zips_won
            count

            
              Donald Trump
              40336130.0
              1104

            
              Joe Biden
              65135275.0
              434

## ca_counties.topo.json

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                stiles
                / ca_counties.topo.json
            
            
              Last active
              June 2, 2020 03:54
            
              
                CA Counties Topojson
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## warn_notice_types.csv

          
            Place
            Permanent
            Temporary
            Unknown

            
              Los Angeles
              226
              1190
              74

            
              Statewide
              704
              2914
              174

## county_total_jobs_change_after.geojson

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                stiles
                / county_total_jobs_change_after.geojson
            
            
              Created
              May 26, 2020 15:18
            
              
                CA County Total Non-farm Employment Change After COVID.geojson
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## claimsout.geojson

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                stiles
                / claimsout.geojson
            
            
              Last active
              May 25, 2020 01:19
            
              
                CA Unemployment Claims Pct Increase, By County
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## totals_by_LA_zips.json

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                stiles
                / totals_by_LA_zips.json
            
            
              Created
              February 24, 2020 03:40
            
              
                2nd District contributions
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## gist:b4571e7d5bba8df386c24fa8c735bda8
{
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "zipcode": "90001", "name": "Los Angeles", "domcounty": "06037", "totpop_cy": 59907, "tothh_cy": 13454, "medage_cy": 27.1, "avghinc_cy": 46931, "avgval_cy": 377591, "tie": 0, "chan_jeong": 0, "herb_wesson": 2500, "jan_perry": 0, "holly_mitchell": 0, "jorge_nuno": 0, "albert_robles": 0, "total": 2500, "winner_dist_two": "Herb Wesson", "winner_total": 2500 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -118.247540000384006, 33.989080000253303 ], [ -118.247760000491994, 33.9893400001475 ], [ -118.247760000491994, 33.9886000017962 ], [ -118.256460000762004, 33.9886000017962 ], [ -118.256460000762004, 33.9893400001475 ], [ -118.259959999703995, 33.989290001891199 ], [ -118.259870000191, 33.987189999401103 ], [ -118.259809999916996, 33.985670000659603 ], [ -118.265190000272, 33.985630000776403 ], [ -118.265190000272, 33.981990001137198 ], [

## pandas.py
# List unique values in a DataFrame column
df['Column Name'].unique()

# To extract a specific column (subset the dataframe), you can use [ ] (brackets) or attribute notation.
df.height
df['height']
# are same thing!!! (from http://www.stephaniehicks.com/learnPython/pages/pandas.html
# -or-
# http://www.datacarpentry.org/python-ecology-lesson/02-index-slice-subset/)
	id,conversation_id,text,source,in_reply_to_user_id,geo,retweet_count,reply_count,like_count,quote_count,date
	1543379427505078273,1543379427505078273,"RT @spicetradeai: $1 in january is now $0.9618 due to inflation

	CPI rose to 292.296 in May from 289.109 in April. To counteract inflation,…",Twitter for iPhone,,,10,0,0,0,2022-07-02
	1543379333573554176,1543378085592137730,@davidjvickery Hehehehe,Twitter for iPhone,4790227152,,0,4,2,0,2022-07-02
	1543378758135992320,1543378085592137730,@IcedKnife Have to keep the lights on,Twitter for iPhone,618539620,,0,0,17,0,2022-07-02
	1543371121906462720,1543369905403088896,@WaspR6 Peoples champ,Twitter for iPhone,941797412380774400,,6,1,770,0,2022-07-02
	1543369905403088896,1543369905403088896,Sit on my face and use my ears as handle bars.,Twitter for iPhone,,,3867,789,24854,2657,2022-07-02
	1543318984933576705,1543318984933576705,"New lisitngs this week came in hot🔥
candidate	raised_in_zips_won	count
Donald Trump	40336130.0	1104
Joe Biden	65135275.0	434
Place	Permanent	Temporary	Unknown
Los Angeles	226	1190	74
Statewide	704	2914	174
	{
	"type": "FeatureCollection",
	"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
	"features": [
	{ "type": "Feature", "properties": { "zipcode": "90001", "name": "Los Angeles", "domcounty": "06037", "totpop_cy": 59907, "tothh_cy": 13454, "medage_cy": 27.1, "avghinc_cy": 46931, "avgval_cy": 377591, "tie": 0, "chan_jeong": 0, "herb_wesson": 2500, "jan_perry": 0, "holly_mitchell": 0, "jorge_nuno": 0, "albert_robles": 0, "total": 2500, "winner_dist_two": "Herb Wesson", "winner_total": 2500 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -118.247540000384006, 33.989080000253303 ], [ -118.247760000491994, 33.9893400001475 ], [ -118.247760000491994, 33.9886000017962 ], [ -118.256460000762004, 33.9886000017962 ], [ -118.256460000762004, 33.9893400001475 ], [ -118.259959999703995, 33.989290001891199 ], [ -118.259870000191, 33.987189999401103 ], [ -118.259809999916996, 33.985670000659603 ], [ -118.265190000272, 33.985630000776403 ], [ -118.265190000272, 33.981990001137198 ], [
	# List unique values in a DataFrame column
	df['Column Name'].unique()

	# To extract a specific column (subset the dataframe), you can use [ ] (brackets) or attribute notation.
	df.height
	df['height']
	# are same thing!!! (from http://www.stephaniehicks.com/learnPython/pages/pandas.html
	# -or-
	# http://www.datacarpentry.org/python-ecology-lesson/02-index-slice-subset/)